Create app.py

app.py

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+import streamlit as st
+import cv2
+import numpy as np
+# Import your chosen deep learning framework (TensorFlow or PyTorch)
+# ...
+import tensorflow
+
+# Load the pre-trained object detection model
+model = cv2.dnn_DetectionModel("path/to/model.weights", "path/to/model.cfg")
+model.setInputParams(size=(416, 416), scale=1/255)
+
+# Optional: Load EasyOCR model if using
+reader = EasyOCR("en")  # Change "en" to your desired language code
+
+def detect_plates(image):
+    # Preprocess image for model input (resizing, normalization, etc.)
+    # ...
+    classes, confidences, boxes = model.detect(image)
+
+    for (class_id, confidence, box) in zip(classes.flatten(), confidences.flatten(), boxes):
+        if class_id == (class_index for class_index in range(len(model.names)) if model.names[class_index] == "license_plate"):  # Adjust class index based on your model
+            x_min, y_min, x_max, y_max = box
+            plate_roi = image[y_min:y_max, x_min:x_max]
+
+            # Perform character recognition (if not using EasyOCR, implement your own)
+            plate_text = "..."
+            if reader is not None:
+                result = reader.readtext(plate_roi)
+                plate_text = result[0][1]
+
+            # Display bounding box and plate text (or confidence score if not using OCR)
+            cv2.rectangle(image, (x_min, y_min), (x_max, y_max), (255, 0, 0), 2)
+            if reader is not None:
+                cv2.putText(image, plate_text, (x_min, y_min - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 0, 0), 2)
+            else:
+                cv2.putText(image, f"Confidence: {confidence:.2f}", (x_min, y_min - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 0, 0), 2)
+
+    return image
+
+def main():
+    """Streamlit app"""
+    st.title("Number Plate Detection App")
+
+    uploaded_file = st.file_uploader("Choose an image", type=["jpg", "jpeg", "png"])
+    if uploaded_file is not None:
+        image = cv2.imdecode(np.fromstring(uploaded_file.read(), np.uint8), cv2.IMREAD_COLOR)
+        results = detect_plates